AF: Small: Collaborative Research: Algorithmic Approaches to Energy-Efficient Computing

AF：小型：协作研究：节能计算的算法方法

• 批准号: 1216993
• 负责人: Fei Li
• 金额: $ 12.83万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216993&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Algorithmic

Energy consumption is now emerging as a dominant performance measure in computer systems. In recent years, significant progress in improving energy efficiency has been accomplished by a combination of better hardware design and software tools. Yet the design of future energy-efficient computer systems will ultimately require the development of fundamental models and algorithmic tools that can be used to guide practical solutions.This project is to study algorithmic methods for improving energy efficiency of data processing and storage in computer systems. The basic approach is to model the operation of various system components in the language of combinatorial optimization, with the objective function representing energy consumption, and to solve these problems using exact or approximate efficient algorithms. Many of those problems can be formulated in terms of task scheduling, where the objective is to optimize the CPU energy consumption required to complete a collection of tasks, while meeting some performance requirements. Other examples include minimizing energy consumption of memory systems, both the internal and external memories, by optimizing power levels and sophisticated paging or caching strategies. In addition to addressing some specific energy optimization problems, this work is expected to produce new algorithmic techniques, as well as deeper understanding of the adequacy of standard performance enhancement tools, like caching and load balancing, for improving energy efficiency. The study on energy complexity will also shed some light on the relation between computation and energy.Some algorithms developed in the course of this research will be implemented, tested empirically on the FreeBSD-based platform, and made available to practitioners. The educational component includes research projects for graduate and undergraduate students, and developing a course on sustainable computing.

能源消耗现在正在成为计算机系统的主要性能指标。近年来，通过更好的硬件设计和软件工具的结合，在提高能源效率方面取得了重大进展。然而，未来节能计算机系统的设计最终将需要开发基本模型和算法工具，这些模型和算法工具可用于指导实际解决方案。本项目旨在研究提高计算机系统数据处理和存储能效的算法方法。其基本方法是用组合优化语言对系统各部件的运行进行建模，以目标函数表示能耗，并使用精确或近似的高效算法求解这些问题。其中许多问题都可以用任务调度来表述，任务调度的目标是优化完成一组任务所需的CPU能耗，同时满足一些性能要求。其他示例包括通过优化电源级别和复杂的分页或缓存策略来最小化内存系统（包括内部和外部内存）的能耗。除了解决一些特定的能源优化问题外，这项工作还有望产生新的算法技术，以及对标准性能增强工具（如缓存和负载平衡）的充分性的更深入理解，以提高能源效率。对能量复杂度的研究也有助于揭示计算与能量之间的关系。在本研究过程中开发的一些算法将在基于freebsd的平台上实现，进行经验测试，并提供给从业者。教育部分包括研究生和本科生的研究项目，以及开发一门关于可持续计算的课程。